{
 "metadata": {
  "name": "",
  "signature": "sha256:e506bd9b1e182748a17e2a3853bb04e20903eaf058aa0752ed6cc614305d3cdb"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import ffn\n",
      "#%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'this is a printed line'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "this is a printed line\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = ffn.get('aapl,msft,yhoo', start='2010-01-01')\n",
      "print data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "             aapl   msft   yhoo\n",
        "Date                           \n",
        "2010-01-04  29.22  27.48  17.10\n",
        "2010-01-05  29.27  27.49  17.23\n",
        "2010-01-06  28.81  27.32  17.17\n",
        "2010-01-07  28.75  27.03  16.70\n",
        "2010-01-08  28.94  27.22  16.70\n",
        "\n",
        "[5 rows x 3 columns]\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>aapl</th>\n",
        "      <th>msft</th>\n",
        "      <th>yhoo</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td> 29.22</td>\n",
        "      <td> 27.48</td>\n",
        "      <td> 17.10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-05</th>\n",
        "      <td> 29.27</td>\n",
        "      <td> 27.49</td>\n",
        "      <td> 17.23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-06</th>\n",
        "      <td> 28.81</td>\n",
        "      <td> 27.32</td>\n",
        "      <td> 17.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-07</th>\n",
        "      <td> 28.75</td>\n",
        "      <td> 27.03</td>\n",
        "      <td> 16.70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-08</th>\n",
        "      <td> 28.94</td>\n",
        "      <td> 27.22</td>\n",
        "      <td> 16.70</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "             aapl   msft   yhoo\n",
        "Date                           \n",
        "2010-01-04  29.22  27.48  17.10\n",
        "2010-01-05  29.27  27.49  17.23\n",
        "2010-01-06  28.81  27.32  17.17\n",
        "2010-01-07  28.75  27.03  16.70\n",
        "2010-01-08  28.94  27.22  16.70\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7fbae88b19d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eeY7JHGhgstRh93qwelyMMJk5bqnjpzvX8NuCom5WGxpUO6zMSs2lMGUEbr+X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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbae88b1610>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this is a comment\n",
      "data.to_returns().dropna().corr().as_format('.2f')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>aapl</th>\n",
        "      <th>msft</th>\n",
        "      <th>yhoo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>aapl</th>\n",
        "      <td> 1.00</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>msft</th>\n",
        "      <td> 0.35</td>\n",
        "      <td> 1.00</td>\n",
        "      <td> 0.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yhoo</th>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.37</td>\n",
        "      <td> 1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "      aapl  msft  yhoo\n",
        "aapl  1.00  0.35  0.28\n",
        "msft  0.35  1.00  0.37\n",
        "yhoo  0.28  0.37  1.00\n",
        "\n",
        "[3 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}